In this paper, we consider a hybrid solution to the sensor network position
inference problem, which combines a real-time filtering system with information
from a more expensive, global inference procedure to improve accuracy and
prevent divergence. Many online solutions for this problem make use of
simplifying assumptions, such as Gaussian noise models and linear system
behaviour and also adopt a filtering strategy which may not use available
information optimally. These assumptions allow near real-time inference, while
also limiting accuracy and introducing the potential for ill-conditioning and
divergence. We consider augmenting a particular real-time estimation method, the
extended Kalman filter (EKF), with a more complex, but more highly accurate,
inference technique based on Markov Chain Monte Carlo (MCMC) methodology.
Conventional MCMC techniques applied to this problem can entail significant and
time consuming computation to achieve convergence. To address this, we propose
an intelligent bootstrapping process and the use of parallel, communicative
chains of different temperatures, commonly referred to as parallel tempering.
The combined approach is shown to provide substantial improvement in a realistic
simulated mapping environment and when applied to a complex physical system
involving a robotic platform moving in an office environment instrumented with a
camera sensor network.